Community Agroecological Values Framework: connecting the community capitals and agroecology to advance northern food system transformation in Kakisa, Northwest Territories, Canada
Bibliographic record
Abstract
Traditional food systems are central to cultural continuity, sustainable livelihoods, and food security for Indigenous communities in northern Canada. However, these systems are threatened by climate change, rising costs, and increasing reliance on purchased foods. At the same time, climate change presents opportunities to diversify through small-scale food production. Such initiatives can enhance food security and self-sufficiency but require tools that integrate Indigenous values and systems thinking. This research introduces the Community Agroecological Values Framework as a novel model for describing local food systems and guiding transformation in northern regions by prioritizing Indigenous values in community-led planning. In partnership with the Ka’a’gee Tu First Nation, this participatory action research uses qualitative methods to describe the current state of the community’s food system and outline a future vision and path to achieving community defined food system goals. Community members highlighted the need for increased access to local foods, greater youth engagement, and support to develop sustainable gardening skills. Insights informed the development of the Community Agroecological Values Framework, which builds on the Community Capitals Framework and Northern Agroecology to create a harmonized, systems-based and values-oriented planning framework and planning tool. This tool centers Traditional Knowledge and cultural values including land stewardship, reciprocal relationships, collective betterment, food sovereignty, self-determination, and intergenerational knowledge sharing, empowering communities to design and implement more resilient, culturally grounded, and self-sufficient food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".